Today, a multitude of content providers offer and deliver a huge selection of media content to users, e.g. by means of downloading or streaming of the media, or by delivering physical discs such as Compact Discs (CDs), Digital Versatile Disc (DVDs) and Blu-ray discs. The media content may include music, films, TV programs and electronic games, which will be generally referred to as “media items” in this description. In order to facilitate and support the user's choice of media items, e.g. for purchase or hire, various solutions and mechanisms have been developed for creating relevant and “targeted” recommendations to users in a communication network, for consuming different media items.
It has become quite common to present recommendations of media items to potential customers from a web-based shop or retailer, where the media recommendations have been somehow enriched to be particularly relevant and adapted to the targeted customers. Media recommendations can also be ranked in order of assumed relevance to the user. The recommendations may thus be presented by various content providers in order to achieve efficiency and impact of their marketing activities and offerings. Thereby, the users will also be better served by receiving more relevant and interesting recommendations which could increase their general responsiveness to such recommendations. The media recommendations are typically created by a network node referred to as “recommendation engine” or “recommender system”, which can provide recommendations to users e.g. via a content provider, an advertiser or the like.
The recommendation engines or systems of today typically employ a filtering mechanism or the like for extracting media items of interest to recommend, which can basically be divided into “content based filtering” and “collaborative filtering”. Briefly described, the content based filtering is configured to determine recommended media items based on information and characteristics of the items and/or the users, while the collaborative filtering is based on ratings of items made by the users. For example, a collaborative filtering algorithm typically determines items to recommend by comparing ratings of items previously made by user and further comparing users with similar characteristics as well. A typical recommendation could be: “customers who bought this product have also bought the following products . . . . ”
In order to produce relevant and potentially interesting media recommendations according to existing solutions, information related to individual users is typically used, such as information on purchased items, consumption behaviour, ratings made, and so forth. When ranked recommendations are used, a highly rated media item is typically deemed to be of higher interest than media items with low or no ratings. However, a media item cannot be ranked on such grounds if no-one has ever bought, hired or made a rating of that item yet, which is e.g. the case for newly released items.
FIG. 1 illustrates how a recommendation of media items can be made for a user 100 according to a conventional procedure. In a first shown action 1:1, a central recommendation system 102 collects information related to users' consumption and ratings of various items on a continuous basis. This type of information is typically available from a communication network 104, e.g. in the form of consumption logs and registered item ratings. In this example, the user 100 first issues a request for a media recommendation to the recommendation system 102, in an action 1:2, e.g. from a user terminal or via a content provider, not shown. In response thereto, system 102 creates a suitable recommendation of items, in an action 1:3, based on the collected information and sends the recommendation to the user 100, in an action 1:4. Hopefully, media items can be selected for the recommendation to be of particular interest to the requesting user, if information on that user is available to the recommendation system 102.
It can be understood from the above that a recommendation system or engine will be able to produce particularly relevant recommendations to individual users if it has access to information on the users, e.g. age, profession, interests, personal taste, previously made purchases and ratings, and so forth. However, to generally protect user privacy, such user information may not be available to a recommendation system which must instead rely on more generic information on consumption and previously made ratings of items. Further, items having no ratings or previous usage, such as newly released ones, will not be found with the conventional filtering methods. It is thus a problem with prior solutions that the selection and ranking of media items in a recommendation require user-related or user-generated information, and such recommendations may not be very relevant, e.g. by missing potentially interesting items, due to lack of basis.